Daniel Gillblad

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While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and(More)
We present a statistical approach to distributed detection of local latency shifts in networked systems. For this purpose, response delay measurements are performed between neighbouring nodes via probing. The expected probe response delay on each connection is statistically modelled via parameter estimation. Adaptation to drifting delays is accounted for by(More)
Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time,(More)
We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through(More)
In this paper we build on methods from probabilistic management for overcoming two issues in the translation of QoS into configurations of network nodes in dynamic, decentralized, and hierarchical networks. First, the inherent uncertainty about node performance in such networks (due to network dynamics) may impede adequate specification of QoS. We suggest(More)
Over the past decade, telecommunication network operators have more and more realized the added value of data analytics for their network deployment efficiency. Early studies targeted the global network perspective by localizing peak loads, both in terms of area and time period. Due to their higher granularity and information richness, current(More)